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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/38804?offset=60</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40940/consed-a-finishing-package-bam-file-viewer-assembly-editor-autofinish-autoreport-autoedit-and-align-reads-to-reference-sequence</guid>
	<pubDate>Fri, 07 Feb 2020 07:16:22 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40940/consed-a-finishing-package-bam-file-viewer-assembly-editor-autofinish-autoreport-autoedit-and-align-reads-to-reference-sequence</link>
	<title><![CDATA[Consed--A Finishing Package (BAM File Viewer, Assembly Editor, Autofinish, Autoreport, Autoedit, and Align Reads To Reference Sequence)]]></title>
	<description><![CDATA[<ul>
<li>Supports Illumina, 454, other Next-Gen and Sanger Reads and allows mixtures of these read types</li>
<li>Consed includes BamScape which can view bam files with unlimited numbers of reads. BamScape can bring up consed to edit reads and the reference sequence in targeted regions.</li>
<li>Consed is compatible with Newbler, Cross_match, Phrap, MIRA, Velvet and PCAP output.</li>
<li>Quickly takes the user to each variant site for viewing (also available as an automated report)</li>
<li>Overview of assembly can help detect and fix misassemblies</li>
<li>Editing time reduced by the program's ability to pin-point problem areas</li>
<li>Editing is guided by error probabilities</li>
</ul><p>Address of the bookmark: <a href="http://www.phrap.org/consed/consed.html" rel="nofollow">http://www.phrap.org/consed/consed.html</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/42633/protocol-for-de-novo-genome-assembly-using-illumina-reads</guid>
	<pubDate>Sat, 16 Jan 2021 21:42:11 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/42633/protocol-for-de-novo-genome-assembly-using-illumina-reads</link>
	<title><![CDATA[Protocol for De novo Genome Assembly using Illumina Reads]]></title>
	<description><![CDATA[<p>In this protocol, we address and describe the de novo assembly method for small to medium-sized genomes.</p><p><strong>What is de novo genome assembly?<br /></strong>The method of taking a large number of short DNA sequences and placing them back together to create a reflection of the original chromosomes from which the DNA originated relates to genome assembly. No previous knowledge of the source DNA sequence length, structure or composition is inferred by De novo genome assemblies. The DNA of the target organism is split up into millions of tiny parts and read on a sequencing computer in a genome sequencing experiment. Depending on the sequencing system used, these "reads" range from 20 to 1000 nucleotide base pairs (bp) in length. Usually, length reads of 36 - 150 bp are produced for Illumina style short read sequencing. These reads can be either &ldquo;single ended&rdquo; as described above or &ldquo;paired end.&rdquo;</p><p><strong>Why genome assembly?</strong><br />In basic research into why and how they live, as well as in applied topics, identifying the DNA sequence of an organism is useful. Awareness of a DNA sequence may be useful in virtually any biological research because of the relevance of DNA to living things. For example, it may be used in medicine to classify, diagnose and eventually improve genetic disorder therapies. Similarly, pathogens study can lead to treatments for infectious diseases.</p><p><strong>Raw NGS data</strong><br />Reads can be saved as a Fasta file as text or in a FastQ file with their attributes.&nbsp;FastQ is the most common read file format since this is what the Illumina sequencing pipeline creates. This will henceforth be the subject of our conversation.</p><p><strong>In a nutshell the protocol:</strong> <br />Get the sequence file(s) read from the sequencing machine (s). <br />Look at the readings - have an idea of what you have and what the standard is like. <br />If required, raw data cleanup/quality trimming. <br />Choose an adequate parameter set for assembly. <br />Assemble the data into scaffolds/contigs. <br />Examine the assembly performance and determine the efficiency of the assembly.</p><p><strong>Read Quality Control:</strong><br />Check the qualiy with fastQC.<br />Script<br />https://bioinformaticsonline.com/snippets/view/42540/install-fastqc-using-conda</p><p>Quality trimming/cleanup of read files.<br />This function trims adapters, barcodes and other contaminants from the reads.<br />Script<br />https://bioinformaticsonline.com/snippets/view/42542/trimmomatic-command</p><p><strong>Genome Assembly:</strong><br />The object of this portion of the protocol is to explain the method of assembling the reads trimmed by quality into draft contigs.</p><blockquote><p>spades.py -1 illumina_R1.fastq.gz -2 illumina_R2.fastq.gz --careful --cov-cutoff auto -o result_of_spades_assembly_all_illumina</p></blockquote><p>A significant range of short-read assemblers are available. Everyone with strengths and disadvantages of their own. <br /><em>Some of the assemblers available include:</em><br />Velvet<br />SOAP-denovo<br />MIRA<br />ALLPATHS</p><p>Next step is to assess the suitability and what to do with a draft package of contiguous details for the remainder of the study now.&nbsp;Few stuff you can note about the contigs you just created:&nbsp;They're the draft Contigs. Any mis-assemblies can occur.</p><p><strong>Mis-assembly checking and assembly metric tools:</strong><br />QUAST - Quality assessment tool for genome assembly http://bioinf.spbau.ru/quast<br />Mauve assembly metrics - http://code.google.com/p/ngopt/wiki/How_To_Score_Genome_Assemblies_with_Mauve<br />InGAP-SV - https://sites.google.com/site/nextgengenomics/ingap and http://ingap.sourceforge.net/<br />inGAP is also useful for finding structural variants between genomes from read mappings.</p><p><strong>Genome finishing tools:</strong><br />Semi-automated gap fillers:<br />Gap filler - http://www.baseclear.com/landingpages/basetools-a-wide-range-of-bioinformatics-solutions/gapfiller/</p><p>IMAGE (V2) - http://sourceforge.net/apps/mediawiki/image2/index.php?title=Main_Page</p><p><strong>Genome visualisers and editors:</strong><br />Artemis - http://www.sanger.ac.uk/resources/software/artemis/<br />IGV - http://www.broadinstitute.org/igv/</p><p><strong>Automated and semi automated annotation tools:</strong><br />Prokka - https://github.com/tseemann/prokka<br />RAST - http://www.nmpdr.org/FIG/wiki/view.cgi/FIG/RapidAnnotationServer<br />JCVI Annotation Service - http://www.jcvi.org/cms/research/projects/annotation-service/</p><p><strong>Frequent command use for the analysis are at:</strong></p><p>https://bioinformaticsonline.com/blog/view/38765/list-of-tools-frequently-used-while-genome-assembly<br />https://bioinformaticsonline.com/pages/view/42275/frequent-parameters-for-bioinformatics-tools</p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43652/peregrine-shimmer-genome-assembly-toolkit</guid>
	<pubDate>Thu, 16 Dec 2021 02:50:19 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43652/peregrine-shimmer-genome-assembly-toolkit</link>
	<title><![CDATA[Peregrine &amp; SHIMMER Genome Assembly Toolkit]]></title>
	<description><![CDATA[<p><span>Peregrine is a fast genome assembler for accurate long reads (length &gt; 10kb, accuracy &gt; 99%). It can assemble a human genome from 30x reads within 20 cpu hours from reads to polished consensus. It uses Sparse HIereachical MimiMizER (SHIMMER) for fast read-to-read overlaping without quadratic comparisions used in other OLC assemblers.</span></p><p>Address of the bookmark: <a href="https://github.com/cschin/Peregrine" rel="nofollow">https://github.com/cschin/Peregrine</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44474/claw-chloroplast-long-read-assembly-workflow</guid>
	<pubDate>Wed, 21 Feb 2024 12:37:46 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44474/claw-chloroplast-long-read-assembly-workflow</link>
	<title><![CDATA[CLAW: Chloroplast Long-read Assembly Workflow]]></title>
	<description><![CDATA[<p dir="auto">CLAW (Chloroplast Long-read Assembly Workflow) is an mostly-automated Snakemake-based workflow for the assembly of chloroplast genomes. CLAW uses chloroplast long-reads, which are baited out of larger read libraries (e.g., an Oxford Nanopore Technologies MinION read library derived from photosynthetic tissue), for assembly with Flye and/or Unicycler. CLAW was designed with the novice bioinformatician in mind - it is easy to install and easy to use, requiring only minimal user input.</p><p>Address of the bookmark: <a href="https://github.com/aaronphillips7493/CLAW" rel="nofollow">https://github.com/aaronphillips7493/CLAW</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/9429/srf-vacancy-at-nipgr</guid>
  <pubDate>Tue, 25 Mar 2014 19:20:44 -0500</pubDate>
  <link></link>
  <title><![CDATA[SRF Vacancy at NIPGR]]></title>
  <description><![CDATA[
<p>Applications are invited from suitable candidates for filling up the purely temporary position of one Senior Research Fellow in DST’s Indo-Australian Joint project (with ICRISAT) entitled “Genomic Approach for Stress Tolerant Chickpea” under the guidance of Dr. Mukesh Jain, Scientist, NIPGR.</p>

<p>(A) Senior Research Fellow (One Post):    Emoluments as per DST/DBT norms.</p>

<p>Candidates having M.Sc. degree (with minimum of 55% marks) or equivalent in Life Sciences/Biotechnology/Bioinformatics/ Molecular Biology or any other related field with minimum of two years of post M.Sc. research experience are eligible to apply. The candidate having computer skill (Linux, Perl, Java, MySQL) and/or experience in advanced molecular biology, next generation sequencing data analysis and molecular markers analysis will be preferred.</p>

<p>The position is completely on temporary basis and co-terminus with the project. The initial appointment will be for one year, which can be curtailed/extended on the basis of assessment of the candidate’s performance and discretion of the Competent Authority. NIPGR reserves the right to select the candidate against the above posts depending upon the qualifications and experience of the candidates. Reservation of posts shall be as per Govt. of India norms.</p>

<p>Eligible candidates may apply by sending hard copy of completed application in the given format with a cover letter showing interest and attested copies of the certificates and proof of research experience. The applications should reach at the address given below within 15 days from the date of the advertisement. The subject line on envelope must be superscribed by “Application for the Post of SRF in DST - AISRF project”.</p>

<p>Note: ONLY hard copy of the application in the given format will be accepted.</p>

<p>Last date April 03, 2014</p>

<p>Dr. Mukesh Jain<br />Staff Scientist<br />National Institute of Plant Genome Research<br />Aruna Asaf Ali Marg, P.O. Box NO. 10531,<br />New Delhi - 110067</p>

<p>Advertisement: http://www.nipgr.res.in/careers/vacancies_latest.php#</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41678/gridss-the-genomic-rearrangement-identification-software-suite</guid>
	<pubDate>Sun, 17 May 2020 10:27:44 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41678/gridss-the-genomic-rearrangement-identification-software-suite</link>
	<title><![CDATA[GRIDSS: the Genomic Rearrangement IDentification Software Suite]]></title>
	<description><![CDATA[<p>GRIDSS is a module software suite containing tools useful for the detection of genomic rearrangements. GRIDSS includes a genome-wide break-end assembler, as well as a structural variation caller for Illumina sequencing data. GRIDSS calls variants based on alignment-guided positional de Bruijn graph genome-wide break-end assembly, split read, and read pair evidence.</p><p>Address of the bookmark: <a href="https://github.com/PapenfussLab/gridss" rel="nofollow">https://github.com/PapenfussLab/gridss</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44900/pegas-a-comprehensive-bioinformatic-solution-for-pathogenic-bacterial-genomic-analysis</guid>
	<pubDate>Mon, 01 Sep 2025 01:18:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44900/pegas-a-comprehensive-bioinformatic-solution-for-pathogenic-bacterial-genomic-analysis</link>
	<title><![CDATA[PeGAS: A Comprehensive Bioinformatic Solution for Pathogenic Bacterial Genomic Analysis]]></title>
	<description><![CDATA[<p><span>This is PeGAS, a powerful bioinformatic tool designed for the seamless quality control, assembly, and annotation of Illumina paired-end reads specific to pathogenic bacteria. This tool integrates state-of-the-art open-source software to provide a streamlined and efficient workflow, ensuring accurate insights into the genomic makeup of pathogenic microbial strains.</span></p>
<p><span><img src="https://github.com/liviurotiul/PeGAS/raw/main/Features.png" alt="image" style="border: 0px;"></span></p><p>Address of the bookmark: <a href="https://github.com/liviurotiul/PeGAS" rel="nofollow">https://github.com/liviurotiul/PeGAS</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34678/svfinder-tool-for-detecting-genomic-rearrangement-form-dna-seq-data</guid>
	<pubDate>Thu, 14 Dec 2017 15:51:40 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34678/svfinder-tool-for-detecting-genomic-rearrangement-form-dna-seq-data</link>
	<title><![CDATA[SVfinder: Tool for detecting genomic rearrangement form DNA-seq data]]></title>
	<description><![CDATA[<p><span>SVfinder provides genome-wide detection of structural variants from next generation paired-end sequencing reads.</span></p><p>Address of the bookmark: <a href="https://github.com/cauyrd/SVfinder" rel="nofollow">https://github.com/cauyrd/SVfinder</a></p>]]></description>
	<dc:creator>Robert M Willioms</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36644/tacoa-taxonomic-classification-of-environmental-genomic-fragments-using-a-kernelized-nearest-neighbor-approach</guid>
	<pubDate>Tue, 15 May 2018 09:52:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36644/tacoa-taxonomic-classification-of-environmental-genomic-fragments-using-a-kernelized-nearest-neighbor-approach</link>
	<title><![CDATA[TACOA: Taxonomic classification of environmental genomic fragments using a kernelized nearest neighbor approach]]></title>
	<description><![CDATA[TACOA is a software that can accurately predict the taxonomic origin of genomic fragments from metagenomic data sets by combining the advantages of the k -NN approach with a smoothing kernel function. 

TACOA can be easily installed and run on a desktop computer, therefore allowing researchers to locally analyze their metagenomic sequence data or integrate it into their pipelines.<p>Address of the bookmark: <a href="http://www.cebitec.uni-bielefeld.de/index.php/2-uncategorised/99-tacoa" rel="nofollow">http://www.cebitec.uni-bielefeld.de/index.php/2-uncategorised/99-tacoa</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37788/s-plot2-creates-an-interactive-two-dimensional-heatmap-of-sequences</guid>
	<pubDate>Fri, 28 Sep 2018 05:36:19 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37788/s-plot2-creates-an-interactive-two-dimensional-heatmap-of-sequences</link>
	<title><![CDATA[S-plot2: creates an interactive, two-dimensional heatmap of sequences]]></title>
	<description><![CDATA[<p><span>S-plot2 creates an interactive, two-dimensional heatmap capturing the similarities and dissimilarities in nucleotide usage between genomic sequences (partial or complete). In S-plot2, whole eukaryotic chromosomes and smaller prokaryotic genomes can be efficiently compared. The tool includes functionality to extract, analyze, and automate BLAST queries of regions of interest within the heatmap. This facilitates the investigation of quickly evolving coding regions, novel coding regions, and laterally transferred elements.</span></p>
<p><span>http://www.putonti-lab.com/uploads/4/5/3/0/45307835/s-plot2_tutorial.pdf</span></p>
<p><span>http://journals.sagepub.com/doi/pdf/10.1177/1176934318797354</span></p><p>Address of the bookmark: <a href="https://bitbucket.org/lkalesinskas/splot" rel="nofollow">https://bitbucket.org/lkalesinskas/splot</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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